A common matching algorithm for pattern matching---sift/surf, haar features, generalized Hough transformation characteristics comparison and analysis

Overview of Recognition algorithms:

Sift/surf based on gray-scale graphs,

First, the establishment of image pyramid, the formation of three-dimensional image space, through the Hessian matrix to obtain the local maximum value of each layer, and then in the extreme point around 26 points for NMS, thus obtaining a rough feature point, and then using two interpolation method to obtain the exact characteristics of the layer (scale), that is to complete the scale unchanged.

Second, select a corresponding neighborhood in the feature point, and find out the main direction, in which sift uses the gradient direction of all the points in a square neighborhood, finds the direction that occupies more than 80%, and the surf chooses the circular neighborhood, and uses the method of active fan to find the main direction of the feature point. Aligning in the main direction completes the rotation unchanged.

Three, the main direction for the axis can be at each feature point to establish coordinates, sift in the feature point selection of a size and scale corresponding to the square area, divided into 16 pieces, statistics each piece along eight direction accounted for the proportion, so the feature points formed a 128-dimensional eigenvector, the image is normalized to complete the strength unchanged , and the surf is divided into 64 pieces, the accumulation of the dx,dy,|dx|,|dy| of each block is counted, and the same 128-dimensional vector is formed, and then normalized, the contrast is completed and the intensity is not changed.

Haar features are also based on gray-scale graphs,

Firstly, the classifier is trained by a large number of object images with obvious Haar feature (rectangle), the classifier is cascade, each level is reserved with the approximate same recognition rate to the next level of candidate object with object characteristics. Each level of the sub-classifier is composed of many Haar features (calculated from the integral image, and save the next position), there are horizontal, vertical, oblique, and each feature with a threshold and two branch values, each sub-classifier with a total threshold value. When the object is recognized, the same integral image is used to prepare for the calculation of the Haar feature, then the window with the same size as the window of the object is trained to traverse the whole image, and then the window is enlarged, and the search object is also traversed; whenever the window is moved to a position, the Haar feature within that window is calculated. After weighting and comparing the threshold values of the Haar features in the classifier to select the left or right branch values, the branch value of the accumulated one level is compared with the threshold of the corresponding level, which is greater than the threshold value before the next round of filtering can be entered. This object is identified by a large probability when the class is passed through the classifier.

The generalized Hough Transform is also based on the grayscale graph,

The use of contour as a feature, the fusion of gradient information, to vote to identify objects, in another article of this blog is discussed in detail, here no longer repeat.

Characteristics and similarities and differences and application occasions:

All of the three algorithms are based on intensity (grayscale) information, which are characteristic methods, but the characteristic of Sift/surf is a kind of characteristic with strong directivity and luminance, which makes it suitable for rigid deformation and slight perspective deformation. Haar feature recognition method with a little bit of artificial intelligence, For objects such as human faces, which have obvious, stable structure, the Haar feature is most suitable, as long as the structure is relatively fixed, even if the distortion of the nonlinear deformation is still recognizable, the generalized Hough transform is exactly the exact match, which can get the parameter information of the object's position direction and so on. The first two methods are basically to obtain the local characteristics and then match one by one, but the local characteristics of the calculation method is different, Sift/surf more complex and relatively stable, the Haar method is relatively simple, biased towards a statistical approach to form characteristics, which also makes it has a certain degree of fuzzy elasticity Generalized Hough transformation is a global feature-contour gradient, but can also be seen as the entire contour of each point position and gradient are characteristics, each point of recognition has contributed to, with a visual vote, see how many votes to determine whether to identify the object.

http://blog.csdn.net/cy513/article/details/4285579

deep analysis of http://blog.csdn.net/cy513/article/details/4414352 Sift/surf algorithm--on the subtlety and insufficiency of SIFT

http://blog.csdn.net/kofsky/article/details/1845757 Harr point Angle Feature classification

A common matching algorithm for pattern matching---sift/surf, haar features, generalized Hough transformation characteristics comparison and analysis